Learning to recommend questions based on user ratings

  • Authors:

  • Ke Sun;Yunbo Cao;Xinying Song;Young-In Song;Xiaolong Wang;Chin-Yew Lin

  • Affiliations:

  • Harbin Institute of Technology, Harbin, China;Shanghai Jiao Tong University, Shanghai, and Microsoft Research Asia, Beijing, China;Harbin Institute of Technology, Harbin, China;Microsoft Research Asia, Beijing, China;Harbin Institute of Technology, Harbin, China;Microsoft Research Asia, Beijing, China

  • Venue:
  • Proceedings of the 18th ACM conference on Information and knowledge management
  • Year:
  • 2009

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Abstract

At community question answering services, users are usually encouraged to rate questions by votes. The questions with the most votes are then recommended and ranked on the top when users browse questions by category. As users are not obligated to rate questions, usually only a small proportion of questions eventually gets rating. Thus, in this paper, we are concerned with learning to recommend questions from user ratings of a limited size. To overcome the data sparsity, we propose to utilize questions without users rating as well. Further, as there exist certain noises within user ratings (the preference of some users expressed in their ratings diverges from that of the majority of users), we design a new algorithm called 'majority-based perceptron algorithm' which can avoid the influence of noisy instances by emphasizing its learning over data instances from the majority users. Experimental results from a large collection of real questions confirm the effectiveness of our proposals.